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MongoDB Education North America

Element451

Customer Service Automation · Marketing Personalization

Using MongoDB Atlas to enhance an AI-powered platform for Element451 According to the World Economic Forum, Powerful AI applications can help educational institutions serve their communities by streamlining tasks and increasing engagement.

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After adopting Search Nodes, the team saw CPU usage go from almost complete saturation to 30% to 40% usage during peak hours, while average execution times improved by 10X, dropping down to 10 to 20 milliseconds.
By taking advantage of the flexibility and scalability of MongoDB Atlas, the company’s developer team also rolled out more than 1,200 updates, including brand-new features and tools, in only a year.
Element451’s database processes 70,000 jobs per minute, and it has helped institutions complete 2 million tasks and process more than 1 million admission applications.
The AI-powered bots on the platform even reduced call volume by 14% through efficient inquiry management, saving the college’s staff more than 36,000 minutes.

Reported impact

Business outcome

Achieving growth for Element451 and its customers Since first adopting MongoDB, Element451 has scaled from 15 customers to more than 200, an 80% year-over-year growth since 2020.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Using MongoDB Atlas to enhance an AI-powered platform for Element451 According to the World Economic Forum, Powerful AI applications can help educational institutions serve their communities by streamlining tasks and increasing engagement. Powering more than 100,000 AI conversations Element451 planned its migration to MongoDB Atlas independently, then optimized its strategy with help from the MongoDB Atlas team. Using MongoDB, Element451 developed an advanced AI platform, improved flexibility, and scaled by 80% year over year — all with the aim of helping higher education organizations reach students more effectively. Now, Element451 uses AI to provide tools and technology to administrative teams to manage communication and data across the student journey — from marketing to prospective students to keeping in touch with alumni. “Using SQL, we needed to preplan everything,” said Petar Djordjevic, chief technology officer at Element451.

Data flow

Business documents, tickets, records, and other operational content stay in MongoDB Atlas Vector Search and MongoDB Atlas Search, where search indexes or vector embeddings are built and then queried in real time to supply grounded results to applications, assistants, or discovery features. Upstream and downstream integrations include AWS, CRM systems.

Solution architecture

4 components · 2 layers
  1. Storage
    • MongoDB Atlas Stores the operational, document, and context data behind Element451's customer-service and assistant workflows.
  2. Serving
    • MongoDB Atlas Vector Search Retrieves semantically relevant context from Element451's data for AI, search, and recommendation workflows.
    • MongoDB Atlas Search Provides relevance-based text retrieval across Element451's documents and application data.
    • Atlas Search Nodes Separates search compute from the operational database so Element451 can scale search independently.

Architecture clues

  • AI agents or assistants are layered on top of the MongoDB data platform.
  • The deployment integrates with AWS.
  • The deployment integrates with CRM systems.
  • The migration approach minimized or avoided downtime during cutover.
  • Vector search is part of the retrieval layer for semantic or generative-AI use cases.

Evidence from the source

“Having the MongoDB Atlas team point out what we were doing well and what we could do better was a big help,”
“It really slowed us down as we were trying to add new types of applications and enrich our data model.”
“Using SQL, we needed to preplan everything,”
“We wanted to focus on building our solution and solving problems for schools, not managing a database,”
Transparency Evidence & methodology 100/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. MongoDB

    Element451 customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/element451
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Sep 23, 2024 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified May 29, 2025 · MongoDB · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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